Information-theoretic software clustering

The majority of the algorithms in the software clustering literature utilize structural information to decompose large software systems. Approaches using other attributes, such as file names or ownership information, have also demonstrated merit. At the same time, existing algorithms commonly deem all attributes of the software artifacts being clustered as equally important, a rather simplistic assumption. Moreover, no method that can assess the usefulness of a particular attribute for clustering purposes has been presented in the literature. In this paper, we present an approach that applies information theoretic techniques in the context of software clustering. Our approach allows for weighting schemes that reflect the importance of various attributes to be applied. We introduce LIMBO, a scalable hierarchical clustering algorithm based on the minimization of information loss when clustering a software system. We also present a method that can assess the usefulness of any nonstructural attribute in a software clustering context. We applied LIMBO to three large software systems in a number of experiments. The results indicate that this approach produces clusterings that come close to decompositions prepared by system experts. Experimental results were also used to validate our usefulness assessment method. Finally, we experimented with well-established weighting schemes from information retrieval, Web search, and data clustering. We report results as to which weighting schemes show merit in the decomposition of software systems.     

Advancing the assessment of automated deception detection systems: Incorporating base rate and cost into system evaluation

In the last two decades, there has been an increased interest in automated deception detection systems (ADDs) for use in screening, although little attention has been paid to the usefulness of these systems. ADDs use various means, both invasive and non‐invasive, to ascertain individual intent to deceive or engage in malicious behaviour. Many papers introducing ADDs use signal detection theory to compare a technique's ability to detect malicious intent with other techniques, but in doing so, they do not include contextual information such as base rate and cost. In this paper, we aim to improve future research by showing how the inclusion of contextual information provides a more realistic picture of the research. Through both theoretical arguments and a real‐data example, we show that especially for those contexts where malicious intent is infrequent (ie, with low base rates of deception) that not factoring in the base rate overestimates the accuracy and therefore usefulness. We conclude with recommendations for how future research should provide a fuller picture of the accuracy and usefulness of ADDs.     

Quantum probabilistically cloning and computation

In this article we make a review on the usefulness of probabilistically cloning and present examples of quantum computation tasks for which quantum cloning offers an advantage which cannot be matched by any approach that does not resort to it. In these quantum computations, one needs to distribute quantum information contained in states about which we have some partial information. To perform quantum computations, one uses state-dependent probabilistic quantum cloning procedure to distribute quantum information in the middle of a quantum computation. And we discuss the achievable efficiencies and the efficient quantum logic network for probabilistic cloning the quantum states used in implementing quantum computation tasks for which cloning provides enhancement in performance.     

A synthesizing framework for technology and content choices for information exchange

In this paper, we develop a synthesizing framework for information exchange. In particular, we identify the drivers of both the content of information exchange as well as the choice of the technology used to facilitate information exchange across multiple organizations. Our first driver is the inter-organizational architecture. Unlike the general architectures developed by Williamson [11] and Adler [1], we focus specifically on the effect of architectures on information exchange. We classify the existing architectures in three dimensions, namely, customization, information sharing or trading and closed or open networks. Such a classification enables us to identify particular architectural characteristics that affect content and technology choices. Our second driver refers to the characteristics of the information, the system, the network and the regulatory environment that affect the credibility and usefulness of information exchange for the participants. Our third driver is the incentive structure in the architecture. Only the information that is deemed to be mutually beneficial by all participants will get exchanged. The incentive structure highlights the cost-benefit trade-offs of individual participants. We also place all the five papers accepted for this special issue within this synthesizing framework. Such positioning allows us to identify potential areas for future research and exploration.     

Predicting user satisfaction,strain and system usage of employee self-services

In this study, we explore attitudinal and behavioural patterns when using employee self-service (ESS) systems by using an expanded technology acceptance model (TAM). We examine the relationship between organizational support and information policy on the one hand and ease of use and usefulness on the other, and then the relationship between ease of use and usefulness with satisfaction, strain and system usage. To explore question order effects, user satisfaction was assessed prior to or after survey items. Data was collected from 517 employees using an ESS system. Results from partial least squares structural equation modelling suggests that (a) organizational support and information policy were positively related to ease of use, (b) usefulness was positively related to satisfaction and system usage, (c) ease of use and usefulness were negatively related to user strain and (d) ease of use fully mediates the relation between organizational support and strain as well as between information policy and strain. Evidence for a question order effect was found with increased satisfaction judgements, when satisfaction was assessed after the survey items. Results are discussed in terms of the theoretical and methodological aspects of the TAM and their implications for ESS system implementation.     

Designing electronic feedback – Analyzing the effects of social presence on perceived feedback usefulness

Feedback interventions, i.e. actions taken by (an) external agent(s) to provide information regarding one׳s task performance, are an important element in motivating and raising performance. Especially the perceived feedback usefulness determines its positive effects. In today׳s digitalized world, feedback is more often given electronically, i.e. computer-mediated or even automated by computer systems. Those feedback interventions׳ effect on perceptions resulting from the difference of communication media is essentially considered by the concept of social presence. However, information systems (IS) research lacks a structured evaluation of possible design choices of feedback media, their influence on the social presence and subsequent effect on the perceived feedback usefulness. To close this research gap, we conduct a laboratory experiment with 43 participants in which we analyze six different design choices for feedback media. We applied a 2×3 experimental design covering the feedback source (human, non-human feedback) and media richness (text, audio, and video). We show that social presence directly and mediated by the perceived trustworthiness of feedback on simple IT-based tasks impacts perceived feedback usefulness. Our study concludes by outlining opportunities for future research and practical implications for human and non-human (i.e. automated) feedback.     

Multi-adjoint t-concept lattices

The t-concept lattice is introduced as a set of triples associated to graded tabular information interpreted in a non-commutative fuzzy logic. Following the general techniques of formal concept analysis, and based on the works by Georgescu and Popescu, given a non-commutative conjunctor it is possible to provide generalizations of the mappings for the intension and the extension in two different ways, and this generates a pair of concept lattices. In this paper, we show that the information common to both concept lattices can be seen as a sublattice of the Cartesian product of both concept lattices. The multi-adjoint framework can be applied to this general t-concept lattice, and its usefulness is illustrated by a working example.     

String analysis technique for shopping path in a supermarket

The sensor network technology developed in recent years has made it possible to accurately track the in-store behavior of customers which was previously indeterminable. The information on the in-store behavior of customers obtained by using this technology, namely information on their shopping path, provides us with useful information concerning the customer’s purchasing behavior. The purpose of this research is to apply character string analysis techniques to shopping path data so as to analyze customers’ in-store behavior, and thereby clarify technical problems with them (the character string analysis techniques) as well as their usability. In this paper we generated character strings on visit patterns to store sections by focusing exclusively on customers stopping by these sections in order to elucidate the visiting patterns of customers who made a large quantity of purchases. In this paper, we were able to discover useful information by using the character string analysis technique EBONSAI, thereby illustrating the usability and usefulness of character string analysis techniques in shopping path analysis.     

Exploring context-awareness for ubiquitous computing in the healthcare domain

Ubiquitous technologies have potentials to play major roles in different real world organizational settings. One of the areas where applying ubiquitous technologies has been given a lot of attention is in the healthcare domain. Here, users are frequently on the move while at the same time relying increasingly on centralized computerized information. In this paper, we explore ubiquitous technologies in the real world through two studies in the healthcare domain. First, we look at the use and usability of a ubiquitous electronic patient record (EPR) system distributed on desktop and laptop computers throughout a large hospital. Secondly, we present an extension to this ubiquitous computing environment in the form of a context-aware mobile computer terminal prototype. The usability of the mobile EPR prototype was evaluated in both laboratory and field settings. Our results indicate that the usefulness of a ubiquitous computing environment supporting work activities in healthcare can benefit from context-aware mobile information access. However, interaction design for such systems must be carefully thought out and thoroughly evaluated. Also, while the use of mobile and stationary computers complement each other very well, we found that the usefulness of ubiquitous computing environments in healthcare may benefit from additional elements such as situated displays at key locations and on key objects, and from seamless integration between the different devices comprising the system as a whole.     

Factors affecting reposting behaviour using a mobile phone-based user-generated-content online community application among Chinese young adults

Mobile phone-based user-generated-content (UGC) online community applications have gained increasing popularity among young generations. However, factors that may affect usage behaviour regarding the applications are not fully investigated. In this study, we employed the Technology Acceptance Model as the basis to explore factors that are able to predict user reposting behaviour with the applications. University students (N?=?322) completed a self-reported questionnaire for measuring the studied constructs after they experienced a high-fidelity prototype of a mobile UGC online community application. Results from path analysis demonstrated that perceived usefulness and attitude towards usage were significant determinants of user reposting intention, with 23% of its variance explained. Perceived usefulness, perceived ease of use and information credibility directly predicted attitude towards usage and accounted for 45% of its variance. Perceived ease of use exerted influence on both perceived usefulness and information credibility. The findings can enhance our understanding of factors that contribute to user reposting behaviour and provide insight into design and implementation strategies to increase the likelihood of user intention to repost information using mobile UGC online community applications.     

Discovering user communities on the Internet using unsupervised machine learning techniques

Interest in the analysis of user behaviour on the Internet has been increasing rapidly, especially since the advent of electronic commerce. In this context, we argue here for the usefulness of constructing communities of users with common behaviour, making use of machine learning techniques. In particular, we assume that the users of any service on the Internet constitute a large community and we aim to construct smaller communities of users with common characteristics. The paper presents the results of three case studies for three different types of Internet service: a digital library, an information broker and a Web site. Particular attention is paid on the different types of information access involved in the three case studies: query-based information retrieval, profile-based information filtering and Web-site navigation. Each type of access imposes different constraints on the representation of the learning task. Two different unsupervised learning methods are evaluated: conceptual clustering and cluster mining. One of our main concerns is the construction of meaningful communities that can be used for improving information access on the Internet. Analysis of the results in the three case studies brings to surface some of the important properties of the task, suggesting the feasibility of a common methodology for the three different types of information access on the Internet.     

The Context of Information Use in a Hospital as Simultaneous Similarity–Difference Relations

In the light of the developing discourse on the relative merits of ‘hard’ and ‘soft’ approaches to information systems development, we present a case study application of a methodology which attempts to dissolve such dualities. Personal Construct Psychology (PCP) offers, as a unity, the construing person who is both biology and culture. PCP argues that both the world and the person’s construct system are phenomenologically real and that the viability of any particular construct system depends only on its usefulness to the construing person. In this study, we used PCP to explore the organisational context of information use and distribution in a large hospital. We used repertory grids, a PCP technique, to elicit from 16 members of staff their personal construals of information from different sources in the hospital. The results highlight the relationship between meaningful information and meaningfully active relationships, a theme which we discuss in terms of the development of the hospital information system and in terms of the value of PCP in dissolving hard–soft dichotomies.     

ASUR++: Supporting the design of mobile mixed systems

In this paper we present ASUR++, a notation for describing, and reasoning about the design of, mobile interactive computer systems that combine physical and digital objects and information: mobile mixed systems. ASUR++ helps a designer to specify the key characteristics of such systems and to focus on the relationship between physical objects and actions and digital information exchanges. Following a brief introduction to the notation, we illustrate its potential usefulness via examples based on the design of an augmented museum gallery. We conclude with a consideration of the integration of ASUR++ into the system development process and its augmentation via associated methods and tools.     

A formal analysis of interest-based negotiation

In multi-agent systems (MAS), negotiation provides a powerful metaphor for automating the allocation and reallocation of resources. Methods for automated negotiation in MAS include auction-based protocols and alternating offer bargaining protocols. Recently, argumentation-based negotiation has been accepted as a promising alternative to such approaches. Interest-based negotiation (IBN) is a form of argumentation-based negotiation in which agents exchange (1) information about their underlying goals; and (2) alternative ways to achieve these goals. However, the usefulness of IBN has been mostly established in the literature by appeal to intuition or by use of specific examples. In this paper, we propose a new formal model for reasoning about interest-based negotiation protocols. We demonstrate the usefulness of this framework by defining and analysing two different IBN protocols. In particular, we characterise conditions that guarantee their advantage (in the sense of expanding the set of individual rational deals) over the more classic proposal-based approaches to negotiation.     

Hierarchy aware distributed plan execution monitoring

Collaborative plan execution is becoming increasingly important given its potential for operational agility and cost reduction. In this paper we propose a distributed and hierarchy aware monitoring procedure for operational plan execution taking place in a dynamic environment characterized by unreliable communication and exogenous events. The contribution of this paper consists in employing a hierarchical clustering approach supporting a multi-party and hierarchy aware information sharing mechanism that is resilient to disruptions in the execution environment. The proposed distributed monitoring procedure uses asymmetric clustering to reflect hierarchical relationships along with gossip based communication across the clusters. Of significance is the information sharing mechanism formalization which utilizes a fresh information window in conjunction with communicating Markov Decision Processes. We show the usefulness of assessing shared information awareness via probabilistic model checking for various combinations of clustering topology and disruption conditions. In this context, we assess formal specifications expressed in probabilistic temporal logic and show how the model checking results can be used to derive the best fresh window value to maximize an information awareness utility function. An illustrative case study is also presented.     

Automatic layout of statecharts

Graphical notations are widely used for system specification. The usefulness of these notations depends primarily on their readability. Hence, automatic methods are needed to obtain efficient and understandable graphical representations of requirements. In this paper, we present an algorithm that automatically generates layouts of statecharts. We assume that relevant information is stored in a structure that we call a decomposition tree, and we draw the graph that models a statechart in a hierarchical fashion. Our approach excludes diagrams with inter‐level transitions. Copyright © 2001 John Wiley & Sons, Ltd.     

SemSearchPro – Using semantics throughout the search process

Semantic search attempts to go beyond the current state of the art in information access by addressing information needs on the semantic level, i.e. considering the meaning of users’ queries and the available resources. In recent years, there have been significant advances in developing and applying semantic technologies to the problem of semantic search. To collate these various approaches and to better understand what the concept of semantic search entails, we study semantic search under a general model. Extending this model, we introduce the notion of process-based semantic search, where semantics is exploited not only for query processing, but might be involved in all steps of the search process. We propose a particular approach that instantiates this process-based model. The usefulness of using semantics throughout the search process is finally assessed via a task-based evaluation performed in a real world scenario.     

Determinants of software quality: A survey of information systems project managers

Software quality is important for the success of any information systems (IS). In this research, we find the determinants of software quality. We used five attributes for software quality: system reliability, maintainability, ease of use, usefulness, and relevance. By surveying 112 IS project managers, we collected data about their perceptions on the software quality attributes and their determinants. We arrived at six factors through exploratory factor analysis. We determined the individual factors that impacted the software quality attributes; for example, reliability is associated with responsiveness of IS department; ease of use is influenced by the capabilities of users and attitude of management; and usefulness is impacted by capabilities of IS department and responsiveness of IS department. We show that organizational factors are more important than technical factors in impacting software quality in IS projects. We provide implications of our research to practice and to future research.     

Technology acceptance model: A replicated test using TETRAD

The technology acceptance model proposes that ease of use and usefulness predict the acceptance of information technology. Since its inception, the model has been widely applied in understanding user acceptance behavior. In a recent study on e‐commerce applications, we surprisingly found that the model was validated when tested in isolation but failed within the larger nomological network. In this paper, we applied TETRAD III to explore the issue. As expected, we discovered two spurious associations in the model and fundamentally challenge its validity and hundreds of related studies. © 2009 Wiley Periodicals, Inc.     

Ontology-based user profile learning

Personal agents gather information about users in a user profile. In this work, we propose a novel ontology-based user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user’s behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.